Generating reasons for imaging studies

ABSTRACT

A framework for generating reasons for imaging studies. An extractor, including a reinforcement learning agent, is trained to select one or more relevant sentences from the training histories of present illness. An abstractor is further pre-trained to generate one or more reasons for study from the one or more relevant sentences. An entity linking system is pre-trained using medical text corpora to map one or more mentions in the one or more reasons for study to one or more standardized medical entities for predicting one or more diagnoses. The reinforcement learning agent may then be re-trained using one or more rewards generated by the entity linking system. One or more reasons for study may be generated from a current history of present illness using the trained extractor, abstractor and entity linking system.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. provisionalapplication No. 63/161,031 filed Mar. 15, 2021, the entire contents ofwhich are herein incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to digital data processing, andmore particularly to generating reasons for imaging studies.

BACKGROUND

Physicians typically order radiology imaging studies to confirm certainabnormalities/disorders during patient care. The physician's reasons forordering the radiology imaging studies (RFS) or reasons for examinations(RFE) are typically found in an electronic medical record (EMR), whichcontains various health-related information associated with a specificpatient in a hospital stay. Such health-related information may includea history of present illness (HPI), as well as reasons for variousradiology imaging studies ordered by physician for that patient at onehospital stay.

It is observed that 10-30% of such physician orders of radiology imagingstudies require revision by radiologists due to various errors. The mostfrequent issue is the misalignment between the reason for the imagingstudy and the diagnosis to be confirmed. Revising the imaging ordersoften means radiologists need to review physician progress notes withinthe EMR to obtain the correct reason for study (RFS). This task can betime-consuming and may be perceived as an interruption to the usualradiology workflow. Additionally, the communication breakdown betweenthe ordering physician and the radiologist may contribute to delays inproviding care, leading to patient dissatisfaction. Furthermore, if theradiologist does not revise the incorrect orders and act on the originalrequests, associated errors in clinical decisions will impact thehospital revenue cycle and can result in significant amounts ofunreimbursed care services.

SUMMARY

Described herein is a framework for generating reasons for imagingstudies. According to one aspect, an extractor, including areinforcement learning agent, is trained to select one or more relevantsentences from the training histories of present illness. An abstractoris further pre-trained to generate one or more reasons for study fromthe one or more relevant sentences. An entity linking system ispre-trained using medical text corpora to map one or more mentions inthe one or more reasons for study to one or more standardized medicalentities for predicting one or more diagnoses. The reinforcementlearning agent may then be re-trained using one or more rewardsgenerated by the entity linking system. One or more reasons for studymay be generated from a current history of present illness using thetrained extractor, abstractor and entity linking system.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings.

FIG. 1 shows an exemplary electronic medical record (EMR);

FIG. 2 is a block diagram illustrating an exemplary system;

FIG. 3 shows an exemplary summarization module;

FIG. 4 shows an exemplary method of training a summarization module togenerate reasons for imaging studies;

FIG. 5 illustrates pre-training of a Bidirectional EncoderRepresentations from Transformers (BERT) masked language model (MLM);and

FIG. 6 shows an exemplary method of generating reasons for imagingstudies.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, devices, methods, etc., inorder to provide a thorough understanding of implementations of thepresent framework. It will be apparent, however, to one skilled in theart that these specific details need not be employed to practiceimplementations of the present framework. In other instances, well-knownmaterials or methods have not been described in detail in order to avoidunnecessarily obscuring implementations of the present framework. Whilethe present framework is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that there is no intent to limit theinvention to the particular forms disclosed; on the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the invention. Furthermore, forease of understanding, certain method steps are delineated as separatesteps; however, these separately delineated steps should not beconstrued as necessarily order dependent in their performance.

Unless stated otherwise as apparent from the following discussion, itwill be appreciated that terms such as “segmenting,” “generating,”“registering,” “determining,” “aligning,” “positioning,” “processing,”“computing,” “selecting,” “estimating,” “detecting,” “tracking” or thelike may refer to the actions and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices. Embodiments of the methods described herein may be implementedusing computer software. If written in a programming language conformingto a recognized standard, sequences of instructions designed toimplement the methods can be compiled for execution on a variety ofhardware platforms and for interface to a variety of operating systems.In addition, implementations of the present framework are not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages may be used.

Medical named entity recognition (NER) is a well-known natural languageprocessing (NLP) technique. Medical NER may be implemented usingtraditional machine learning techniques, such as hidden Markov model(HMI), conditional random fields (CRF), more recent deep learningtechniques (e.g., recurrent neural networks (RNN), long short termmemory (LSTM) etc.), or combination of techniques (e.g. LSTM-CRF).Medical NER tasks often involve extracting single or multiple clinicalterms from text. A direct application of medical NER on HPI willrecognize many explicitly mentioned entity types, such as symptoms,findings or syndromes/diseases. However, medical NER will not recognizeother implicitly associated suspected conditions (and correspondingentity types) that require attention and diagnosis, such as pulmonaryinfiltrate, pneumonia, consolidation, effusion, etc.

Text summarization is an NLP task of producing a short version of asource document while preserving its salient information. There are twotypes of text summarization: extractive and abstractive. Extractive textsummarization involves pulling salient text or keyphrases from thesource document and compiling the salient text to make a summary.Applying extractive summarization on medical text helps to identify onlyrelevant text and corresponding entity types. However, like Medical NER,extractive summarization misses identifying suspected conditions.Abstractive summarization techniques generate the summary bottom up andentail paraphrasing and shortening parts of the source document.Abstractive text summarization algorithms create new content (e.g.,inferred text) that relays the most useful information from the originaltext. Therefore, abstraction performs better than extraction, because itcan generate suspected conditions/diagnoses. However, abstractivesummarization requires a significant amount of labelled training dataand this magnifies with the increase in level of abstraction (lowoverlap) between source text and target summary. For radiologyexaminations, translating HPI to reasons for study (RFS) is highlyabstractive and also lacks sufficient labels.

FIG. 1 shows an exemplary EMR 102. The top section 104 shows a historyof present illness (HPI) during a hospital stay. The middle section 106shows a list of reasons for various radiology imaging studies ordered byphysician for that patient at one hospital stay. The bottom section 108lists standardized signs, symptoms and diagnoses that are mentioned inthe imaging studies for which the hospital raises reimbursementrequests. The indications/diagnoses for the imaging study (shown inmiddle section 106) require a strong understanding of signs/symptoms inthe HPI (shown in upper section 104). For example, r/o infiltrate (i.e.,suspicion of pulmonary infiltrate) can be inferred from points 1 and 7of the observed HPI in section 104. Similarly, inadequate ventilation oroxygenation from points 1, 2, and 7 of the observed HPI in section 104imply usage of NGT (nasogastric tube) and its placement verification asshown in section 106.

Thus, unlike many abstractive summarization tasks, RFS are a collectionof highly abstractive singular phrases or sentences, such as “evaluatefor PNA, consolidation, effusion” or “r/o infiltrate failure”, or evenjust single word “infiltrate”. Automatically generating such abstractivetext requires document level inference, abstraction and paraphrasing.Each physician progress note typically has sections of text with mainsymptoms and diseases. For example, point 1 in the HPI (shown in uppersection 104) with text “ . . . with a history of emphysema (not on homeO2), who presents with three days of shortness of breath thought by herprimary care doctor to be a COPD flare”, guides physicians to order anexam and give a reason (e.g., “evaluate for PNA, consolidation,effusion”). It becomes easy for the model to generate relevant RFS if itis guided to focus on such sections of text in the HPI. Additionally, itis equally important for the RFS generation system to be intelligentenough to disambiguate abbreviated medical terms, such as COPD (chronicobstructive pulmonary disease) and R/O (rule out), and knowledgeableabout the subtleties associated with medical terms.

A framework for automatically generating reasons for imaging studies(RFS) is presented herein. The present framework employs a two-stepsummarization method (i.e., extractive followed by abstractive) anddivides the overall summarization method into two subtasks: distillingimportant texts (extractive) followed by paraphrasing them(abstractive). However, overlap between HPI-RFS is so low that simpleoverlaps between RFS and HPI are insufficient to guide the two-stepmodel. Therefore, the present framework utilizes an external medicalknowledge base through an entity linking system. This entity linkingsystem links diagnoses in the generated text to a standard ontology andvalidates the number of accurately mapped diagnoses. Thus, it addressesthe variations in medical terms and indirectly measures the relevancy ofgenerated text. The entity linking system is integrated into thetwo-step summarization module through a reinforcement learning (RL)technique. Rewards for the reinforcement learning are determined byevaluating the quality of the RFS generated by the abstractor usingentity linking.

The present framework address the issues of misalignment betweendiagnoses and imaging study requests, and minimizes delay in providingcare by automatically generating RFS using the HPI. The presentframework combines both extractive and abstractive text summarizationtechniques and also employs entity-linking in order to ensure that theright context related to medical terms in RFS and suspected conditionsare included in the generated results. Pretraining of individualcomponents addresses the issue of compiling a large number of HPI-RFSpairs for training a highly abstractive system, enabling the frameworkto learn from a small size of such pairs. These and other features andadvantages will be described in more details herein.

FIG. 2 is a block diagram illustrating an exemplary system 200. Thesystem 200 includes a computer system 201 for implementing the frameworkas described herein. In some implementations, computer system 201operates as a standalone device. In other implementations, computersystem 201 may be connected (e.g., using a network) to other machines,such as information source 230 and workstation 234. In a networkeddeployment, computer system 201 may operate in the capacity of a server(e.g., thin-client server), a cloud computing platform, a client usermachine in server-client user network environment, or as a peer machinein a peer-to-peer (or distributed) network environment.

In some implementations, computer system 201 comprises a processor orcentral processing unit (CPU) 204 coupled to one or more non-transitorycomputer-readable media 206 (e.g., computer storage or memory), displaydevice 208 (e.g., monitor) and various input devices 209 (e.g., mouse orkeyboard) via an input-output interface 221. Computer system 201 mayfurther include support circuits such as a cache, a power supply, clockcircuits and a communications bus. Various other peripheral devices,such as additional data storage devices and printing devices, may alsobe connected to the computer system 201.

The present technology may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof, either as part of the microinstruction code or as part of anapplication program or software product, or a combination thereof, whichis executed via the operating system. In some implementations, thetechniques described herein are implemented as computer-readable programcode tangibly embodied in non-transitory computer-readable media 206. Inparticular, the present techniques may be implemented by summarizationmodule 217.

Non-transitory computer-readable media 206 may include random accessmemory (RAM), read-only memory (ROM), magnetic floppy disk, flashmemory, and other types of memories, or a combination thereof. Thecomputer-readable program code is executed by CPU 204 to process medicaldata retrieved from, for example, information source 230. As such, thecomputer system 201 is a general-purpose computer system that becomes aspecific purpose computer system when executing the computer-readableprogram code. The computer-readable program code is not intended to belimited to any particular programming language and implementationthereof. It will be appreciated that a variety of programming languagesand coding thereof may be used to implement the teachings of thedisclosure contained herein.

The same or different computer-readable media 206 may be used forstoring a database (or dataset). Such data may also be stored inexternal information source 230 or other memories. Information source230 may store medical images acquired by a radiology imaging device(e.g., MR or CT scanner) as well as electronic medical records (EMRs)and other types of information. Such EMRs may include health-relatedinformation associated with specific patients, including histories ofpresent illness (HPIs). Information source 230 may be implemented usinga database management system (DBMS) managed by the CPU 204 and residingon a memory, such as a hard disk, RAM, or removable media. Informationsource 230 may be implemented on one or more additional computersystems. For example, information source 230 may include a datawarehouse system residing on a separate computer system, a cloudplatform or system, an EMR system, or any other hospital, medicalinstitution, medical office, testing facility, pharmacy or other medicalpatient record storage system.

The workstation 234 may include a computer and appropriate peripherals,such as a keyboard and display device, and can be operated inconjunction with the entire system 200. For example, the workstation 234may communicate directly or indirectly with the information source 230so that medical information may be displayed at the workstation 234 andviewed on a display device. The workstation 234 may include a graphicaluser interface to receive user input via an input device (e.g.,keyboard, mouse, touch screen voice or video recognition interface,etc.).

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystems components (or the process steps) may differ depending upon themanner in which the present framework is programmed. Given the teachingsprovided herein, one of ordinary skill in the related art will be ableto contemplate these and similar implementations or configurations ofthe present framework.

FIG. 3 shows an exemplary summarization module 217. Summarization module217 includes an extractor 302, an abstractor 304, an entity linkingsystem 306 and a reinforcement learning (RL) agent 308. Extractor 302receives as input a discharge summary 310 or any other EMR of a specificpatient. The discharge summary 310 includes a history of present illness(HPI), that is made up of multiple sentences 51, S2, S3, S4, Sn. Eachsentence S includes one or more words that describe the illness.Extractor 302 is trained to extract relevant sentences (e.g., S1, S4,S5) 312 out of the HPI. Abstractor 304 is trained to generate reasonsfor imaging study (RFS) 314 from the extracted sentences 312. RFS 314includes one or more reasons (e.g., R1, R2) for ordering a radiologystudy, examination or report for the patient.

The generated RFS 314 is passed through an entity linking system 306.Entity linking system 306 is an unsupervised zero or few shotentity-linking model that does not require a separate entity-linkingcorpus for training, which is an expensive and time-consuming process.The entity linking system 306 takes the RFS 314, detects mentions andmaps them to a dictionary (or ontology) of standard medical entities(e.g., Systematized Nomenclature of Medicine or SNOMED) A “mention” asused herein is a reference or representation of an entity or an objectthat appeared in texts. The entity linking system 306 finds all thenormalized/standardized entities associated with the RFS 314. Entitylinking system 306 includes a pre-trained transformer-based contextuallanguage model that obtains representations of mention text and windowsof text on left and right of the mention. Entity linking system 306 thencombines these representations and learns to map it to appropriatestandardized entities in the dictionary. Such entities are output aspredicted diagnoses (e.g., DP1, DP2, DP3) 316.

The reinforcement learning (RL) agent 308 may be implemented in theextractor 302, and is trained using actor-critic reinforcement learningtechnique. The aim of the reinforcement learning (RL) agent 308 is toselect relevant sentences 312 from the source HPI 310 and is rewardedbased on the quality of generated RFS 314 from the abstractor 304. Insome implementations, entity linking system 306 measures the overlap(e.g., percentage overlap) between the generated predicted diagnoses 316and the reference or actual diagnoses (e.g., DA1, DA2, DA3, DA4) 322 togenerate the reward (e.g., Rewardl) 320 that is sent to thereinforcement learning (RL) agent 308. Another type of reward (e.g.,Reward2) 320 may be generated based on the overlap between the actualRFS (e.g., r1, r2, r3) 318 and the RFS 314 generated by the abstractor304. Other methods of reward generation may also be used.

FIG. 4 shows an exemplary method 400 of training a summarization moduleto generate reasons for imaging studies. It should be understood thatthe steps of the method 400 may be performed in the order shown or adifferent order. Additional, different, or fewer steps may also beprovided. Further, the method 400 may be implemented with the system 200of FIG. 2, the summarization module 217 of FIG. 3, a different system,or a combination thereof.

At 402, summarization module 217 receives training HPIs, referencemedical documents and a medical text corpora. The training HPIs may beextracted from discharge summaries, physician progress notes, EMRs orother medical documents for specific patients. The reference medicaldocuments are used for evaluating the quality of the RFS generated bythe abstractor 304. In some implementations, the reference medicaldocuments include actual diagnoses and/or radiology reports with actualRFS.

The medical text corpora include texts collected with a focus on themedical domains. The medical text corpora may be compiled out of freetext from sources (e.g., the Web) by using regular expressions createdbased on entries in standard medical ontologies (e.g., SystematizedNomenclature of Medicine or SNOMED). The medical text corpora may bepreprocessed by using natural language processing tools, such asStanford CoreNLP or ScispaCy. See, for example, Manning, Christopher D.,Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and DavidMcClosky, The Stanford CoreNLP Natural Language Processing Toolkit,Proceedings of the 52nd Annual Meeting of the Association forComputational Linguistics: System Demonstrations, pp. 55-60 (2014);Neumann, Mark, Daniel King, Iz Beltagy and Waleed Ammar, ScispaCy: Fastand Robust Models for Biomedical Natural Language Processing, ArXivabs/1902.07669 (2019).

At 404, a transformer-based contextual language model is pre-trainedbased on the medical text corpora. The transformer-based contextuallanguage model may be used in extractor 302 and/or abstractor 304. Thetransformer-based contextual language model is an unsupervised languagerepresentation deep learning model that is trained based on the medicaltext corpora to map input text to representations. See, for example,Ashish Vaswani et al., Attention is all you need, Advances in NeuralInformation Processing Systems (NIPS) 2017, which is herein incorporatedby reference.

One example of a transformer-based contextual language model is theBidirectional Encoder Representations from Transformers (BERT). See, forexample, Jacob Devlin et al., BERT: Pre-training of deep bidirectionaltransformers for language understanding, North American Association forComputational Linguistics (NAACL) 2019, which is herein incorporated byreference. BERT optimizes two training objectives—masked language model(MLM) and next sentence prediction (NSP). FIG. 5 illustrates apre-training process 500 of a BERT masked language model (MLM) 504 usinga large corpus of medical text 502 to generate words and sentences invector representations 506. Output X7 and X8 are masked words“gastrointestinal” and “bleed” 508 respectively.

Returning to FIG. 4, at 406, summarization module 217 generatessource-target pairs from the training HPIs and reference medicaldocuments. Each source-target pair includes a HPI and a correspondingactual RFS associated with a specific patient and a specific hospitalstay. The actual RFS may be extracted from a radiology report in thereference medical documents.

At 408, extractor 302 is trained based on the source-target pairs usingone or more heuristics. Extractor 302 is trained to extract relevantsentences out of the HPIs. Heuristics that may be used to train theextractor 302 include, for example, using the overlap betweenindications in the HPIs and the actual RFS to guide the training.

At 410, abstractor 304 is pre-trained based on the source-target pairsusing one or more heuristics to generate RFS from the sentences selectedby extractor 302. The one or more heuristics include, for example,token-overlap above a predetermined threshold to guide the pre-training.In some implementations, abstractor 304 is a deep transformer-basedencoder-decoder model, where the encoder and decoder weights arepretrained with self-supervised pre-training designed for abstractivetext summarization. See, for example, Zhang, Jingqing, et al. “Pegasus:Pre-training with extracted gap-sentences for abstractivesummarization.” arXiv preprint arXiv:1912.08777 (2019), which is hereinincorporated by reference.

Pre-training tailored for abstractive text summarization selects andmasks whole sentences from documents and concatenates the gap-sentencesinto a pseudo-summary. This training process is known as gap sentencesgeneration (GSG) and has a hyper-parameter gap sentences ratio (GSR).The GSR refers to the ratio of the number of selected gap sentences tothe total number of sentences in the document, which is similar to maskrate in MLM. In abstractor 304, both gap sentences and GSR are selectedusing the medical knowledge in the pre-training medical text corpora.Gap sentences should have mention of at least one entity from a curatedlist of allowed medical entities (e.g., symptoms).

At 412, entity linking system 306 is pre-trained using the medical textcorpora. Entity linking system 306 is pre-trained to detect one or morementions in the RFS generated by abstractor 304 and map the one or morementions to one or more standardized medical entities in the medicaltext corpora, so as to predict one or more diagnoses 316. Entity linkingsystem 306 may include a pre-trained transformer-based contextuallanguage model. Mention text and windows of text on the left and rightof each mention are passed through a pre-trained contextual languagemodel to obtain representations. Entity linking system 306 then combinesthese representations and learns to map it to appropriate entities inthe dictionary (or ontology). Entity linking system 306 providesunsupervised zero or few shot entity linking, since it is only trainedon a corpus of different biological domains and directly applied toselectively map RFS mentions to standard medical entities in a standardmedical ontology.

A mention detection component in the entity linking system 306 may betrained using minimal supervision, using, for instance, vocabulary fromSNOMED and building rules. Machine learning techniques, such assemi-supervised learning and transfer learning, may also be used.Similarly, a linking component in the entity linking system 306 may alsobe trained using zero or few shot learning techniques in combinationwith transfer learning. There are several related corpora fortransfer-learning, such as The National Center for BiotechnologyInformation (NCBI) Disease Corpus, which is a resource for disease namerecognition and concept normalization.

At 414, it is determined if there is any source-target pair to beprocessed. If there is, the method proceeds to 416 to process the nextsource-target pair as the current source-target pair. If there are nofurther source-target pair to be processed, the method proceeds to 424.

At 416, sentences are selected from the current source-target pair usingthe trained extractor 302. The trained extractor provides an RL agent308. The aim of the RL agent 308 is to select relevant sentences fromthe source HPI, and receive a feedback of rewards based on the qualityof the RFS generated by the abstractor 304.

At 418, the trained abstractor 304 is applied on the selected sentencesto generate RFS. The RFS includes one or more paraphrases of theselected sentences to convey the most useful information from theoriginal text.

At 420, entity linking system 306 is applied to the generated RFS togenerate predicted diagnoses. Basically, since several medical terms areexpressed in different forms (e.g., “pulmonary infiltrates” is alsowritten as “infiltrates” and “Pneumonia” as “PNM”), this implies thatthe model can generate a different form of an indication in HPI than thereference RFS. Entity linking system 306 detects mentions in thegenerated RFS, maps the mentions to normalized (or standardized)entities using a dictionary (or ontology) and generates predicteddiagnoses 316 as output. In other words, the predicted diagnoses areindications in standard ontology (e.g., SNOMED) that are determined bylinking relevant phrases in the generated RFS using the entity linkingsystem 306.

At 422, entity linking system 306 determines rewards and re-trains theRL agent 308 using an actor critic technique. The actor critic techniqueis a temporal difference (TD) method that has a separate memorystructure (i.e., actor) to explicitly represent the policy independentof the value function (i.e., critic). The rewards for the value functionmay be determined by measuring the overlap (e.g., percentage overlap)between the predicted diagnoses and actual diagnoses extracted from thereference medical documents. Additionally, the rewards 320 may also bedetermined by measuring the overlap (e.g., percentage overlap) betweenthe generated RFS and the actual RFS extracted from the referencemedical documents.

At 424, the trained extractor 302 with the trained RL agent 308, thetrained abstractor 304 and trained entity linking system 306 are output.The trained extractor 302, trained RL agent 308, trained abstractor 304and trained entity linking system 306 may be applied to a currenthistory of present illness associated with a current patient to generateone or more reasons for study.

FIG. 6 shows an exemplary method 600 of generating reasons for imagingstudies. It should be understood that the steps of the method 600 may beperformed in the order shown or a different order. Additional,different, or fewer steps may also be provided. Further, the method 600may be implemented with the system 200 of FIG. 2, the summarizationmodule 217 of FIG. 3, a different system, or a combination thereof.

At 604, the trained extractor 302 with the trained RL agent 308, thetrained abstractor 304, the trained entity linking system 306 and acurrent HPI are received. The current HPI may be extracted from amedical document that is associated with a specific patient for aspecific hospital stay. The current medical document may be, forexample, a discharge summary, physician progress notes or other EMR.

At 606, the trained extractor 302 selects one or more sentences from thecurrent HPI.

At 608, the trained abstractor 304 generates one or more RFS from theone or more selected sentences.

At 610, the entity linking system 306 predicts one or more diagnosesfrom the one or more generated RFS. More particularly, the entitylinking system 306 detects one or more mentions in the generated RFS andmaps them to standardized entities in a dictionary (or ontology) tocompile one or more predicted diagnoses.

While the present framework has been described in detail with referenceto exemplary embodiments, those skilled in the art will appreciate thatvarious modifications and substitutions can be made thereto withoutdeparting from the spirit and scope of the invention as set forth in theappended claims. For example, elements and/or features of differentexemplary embodiments may be combined with each other and/or substitutedfor each other within the scope of this disclosure and appended claims.

What is claimed is:
 1. A medical text summarization system, comprising:a non-transitory memory device for storing computer readable programcode; and a processor device in communication with the memory device,the processor being operative with the computer readable program code toperform steps including receiving training histories of present illness,reference medical documents and medical text corpora, training, based onthe training histories of present illness and the reference medicaldocuments, an extractor to select one or more relevant sentences fromthe training histories of present illness, wherein the extractorcomprises a reinforcement learning agent, pre-training, based on thetraining histories of present illness and the reference medicaldocuments, an abstractor to generate one or more reasons for study fromthe one or more relevant sentences, pre-training an entity linkingsystem using the medical text corpora to map one or more mentions in theone or more reasons for study to one or more standardized entities forpredicting one or more diagnoses, re-training, based on the traininghistories of present illness and the reference medical documents, thereinforcement learning agent using one or more rewards generated by theentity linking system, and generating one or more reasons for study froma current history of present illness using the trained extractor, thepre-trained abstractor and the pre-trained entity linking system.
 2. Themedical text summarization system of claim 1 wherein the processor isoperative with the computer readable program code to generatesource-target pairs from the training histories of present illness andthe reference medical documents for training the extractor andpre-training the abstractor.
 3. The medical text summarization system ofclaim 1 wherein the processor is operative with the computer readableprogram code to pre-train a transformer-based contextual language modelusing the medical text corpora.
 4. The medical text summarization systemof claim 3 wherein the transformer-based contextual language model isused in the extractor, the abstractor, or a combination thereof.
 5. Themedical text summarization system of claim 1 wherein the processor isoperative with the computer readable program code to train the extractorusing one or more heuristics.
 6. The medical text summarization systemof claim 1 wherein the processor is operative with the computer readableprogram code to pre-train the abstractor using one or more heuristics.7. The medical text summarization system of claim 2 wherein theprocessor is operative with the computer readable program code tore-train the reinforcement learning agent by selecting, by theextractor, one or more sentences from at least one of the source-targetpairs, generating, by the abstractor, one or more reasons for study fromthe selected one or more sentences, predicting, by the entity linkingsystem, one or more diagnoses from the generated one or more reasons forstudy, and determining, by the entity linking system, the one or morerewards for re-training the reinforcement learning agent using an actorcritic technique.
 8. The medical text summarization system of claim 7wherein the processor is operative with the computer readable programcode to determine the one or more rewards by measuring an overlapbetween the one or more diagnoses predicted by the entity linking systemand reference diagnoses extracted from the reference medical documents.9. The medical text summarization system of claim 7 wherein theprocessor is operative with the computer readable program code todetermine the one or more rewards by measuring an overlap between theone or more reasons for study generated by the abstractor and one ormore actual reasons for study extracted from the reference medicaldocuments.
 10. A method of medical text summarization, comprising:receiving an extractor with a reinforcement learning agent, aabstractor, an entity linking system and a current history of presentillness; selecting, using the trained extractor, one or more sentencesfrom the current history of present illness; generating, using theabstractor, one or more reasons for study from the one or more selectedsentences; and predicting, using the entity linking system, one or morediagnoses from the one or more generated reasons for study.
 11. Themethod of claim 1 further comprises extracting the current history ofpresent illness from a current medical document associated with aspecific patient. The method of claim 10 wherein predicting the one ormore diagnoses comprises mapping one or more mentions in the one or moregenerated reasons for study to one or more standardized entities. 13.The method of claim 10 further comprises pre-training atransformer-based contextual language model using medical text corporafor use in the trained extractor, the pre-trained abstractor, or acombination thereof.
 14. The method of claim 10 further comprisestraining the extractor using one or more heuristics.
 15. The method ofclaim 10 further comprises pre-training the abstractor using one or moreheuristics.
 16. The method of claim 10 further comprises re-training thereinforcement learning agent using one or more rewards generated by theentity linking system.
 17. The method of claim 16 wherein re-trainingthe reinforcement learning agent comprises: selecting, by the extractor,one or more sentences from at least one source-target pair; generating,by the abstractor, one or more reasons for study from the selected oneor more sentences; predicting, by the entity linking system, one or morediagnoses from the generated one or more reasons for study; anddetermining, by the entity linking system, the one or more rewards forre-training the reinforcement learning agent using an actor critictechnique.
 18. The method of claim 17 further comprises determining theone or more rewards by measuring an overlap between the one or morediagnoses predicted by the entity linking system and referencediagnoses.
 19. The method of claim 17 further comprises determining theone or more rewards by measuring an overlap between the one or morereasons for study generated by the abstractor and one or more actualreasons for study.
 20. One or more non-transitory computer-readablemedia embodying instructions executable by machine to performoperations, comprising: receiving an extractor with a reinforcementlearning agent, an abstractor, an entity linking system and a currenthistory of present illness; selecting, using the extractor, one or moresentences from the current history of present illness; generating, usingthe abstractor, one or more reasons for study from the one or moreselected sentences; and predicting, using the entity linking system, oneor more diagnoses from the one or more generated reasons for study.